



"""simple docstring"""





from datetime import datetime as dt
import os

from github import Github


__lowerCAmelCase							:       Union[str, Any]          =       [
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]
def 						__lowerCAmelCase    (		):


						'''simple docstring'''






						snake_case_ :			Any            =      Github(os.environ["""GITHUB_TOKEN"""]     )
						snake_case_ :			Any            =      g.get_repo("""huggingface/transformers"""     )
						snake_case_ :			Any            =      repo.get_issues(state="""open"""     )

						for issue in open_issues:
												snake_case_ :			Union[str, Any]            =      sorted([comment for comment in issue.get_comments()]       ,			key=lambda __UpperCamelCase     : i.created_at       ,			reverse=__UpperCamelCase     )
												snake_case_ :			Dict            =      comments[0] if len(__UpperCamelCase     ) > 0 else None
												if (
												    last_comment is not None
												    and last_comment.user.login == "github-actions[bot]"
												    and (dt.utcnow() - issue.updated_at).days > 7
												    and (dt.utcnow() - issue.created_at).days >= 3_0
												    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()     )
												):
																		# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
																		issue.edit(state="""closed"""     )
												elif (
												    (dt.utcnow() - issue.updated_at).days > 2_3
												    and (dt.utcnow() - issue.created_at).days >= 3_0
												    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()     )
												):
																		# print(f"Would add stale comment to {issue.number}")
																		issue.create_comment(
																		    """This issue has been automatically marked as stale because it has not had """
																		    """recent activity. If you think this still needs to be addressed """
																		    """please comment on this thread.\n\nPlease note that issues that do not follow the """
																		    """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
																		    """are likely to be ignored."""     )


if __name__ == "__main__":
					main()





import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
 import jax

 from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel

class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case=1_3     ,   __snake_case=3_0     ,   __snake_case=2     ,   __snake_case=3     ,   __snake_case=True     ,   __snake_case=True     ,   __snake_case=3_2     ,   __snake_case=5     ,   __snake_case=4     ,   __snake_case=3_7     ,   __snake_case="gelu"     ,   __snake_case=0.1     ,   __snake_case=0.1     ,   __snake_case=1_0     ,   __snake_case=0.02     ,   ):
         snake_case											=							parent
         snake_case											=							batch_size
         snake_case											=							image_size
         snake_case											=							patch_size
         snake_case											=							num_channels
         snake_case											=							is_training
         snake_case											=							use_labels
         snake_case											=							hidden_size
         snake_case											=							num_hidden_layers
         snake_case											=							num_attention_heads
         snake_case											=							intermediate_size
         snake_case											=							hidden_act
         snake_case											=							hidden_dropout_prob
         snake_case											=							attention_probs_dropout_prob
         snake_case											=							type_sequence_label_size
         snake_case											=							initializer_range

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         snake_case											=							(image_size // patch_size) ** 2
         snake_case											=							num_patches + 1
    def 						a_     (			self       ):
         snake_case											=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

         snake_case											=							ViTConfig(
             image_size=self.image_size     ,   patch_size=self.patch_size     ,   num_channels=self.num_channels     ,   hidden_size=self.hidden_size     ,   num_hidden_layers=self.num_hidden_layers     ,   num_attention_heads=self.num_attention_heads     ,   intermediate_size=self.intermediate_size     ,   hidden_act=self.hidden_act     ,   hidden_dropout_prob=self.hidden_dropout_prob     ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,   is_decoder=__snake_case     ,   initializer_range=self.initializer_range     ,   )

         return config, pixel_values
    def 						a_     (			self     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							FlaxViTModel(config=__snake_case       )
         snake_case											=							model(__snake_case       )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         snake_case											=							(self.image_size, self.image_size)
         snake_case											=							(self.patch_size, self.patch_size)
         snake_case											=							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape     ,   (self.batch_size, num_patches + 1, self.hidden_size)       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							self.type_sequence_label_size
         snake_case											=							FlaxViTForImageClassification(config=__snake_case       )
         snake_case											=							model(__snake_case       )
         self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.type_sequence_label_size)       )

         # test greyscale images
         snake_case											=							1
         snake_case											=							FlaxViTForImageClassification(__snake_case       )

         snake_case											=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
         snake_case											=							model(__snake_case       )




    def 						a_     (			self       ):
         snake_case											=							self.prepare_config_and_inputs()
         (
             (
             snake_case
         )					,    (
             snake_case
         )					,    
         )											=							config_and_inputs
         snake_case											=							{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_flax
class     A__   (       snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
    def 						a_     (			self       ):
         snake_case											=							FlaxViTModelTester(self       )
         snake_case											=							ConfigTester(self     ,   config_class=__snake_case     ,   has_text_modality=__snake_case     ,   hidden_size=3_7       )
    def 						a_     (			self       ):
         self.config_tester.run_common_tests()
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__snake_case       )
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case											=							model_class(__snake_case       )
              snake_case											=							inspect.signature(model.__call__       )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              snake_case											=							[*signature.parameters.keys()]

              snake_case											=							['''pixel_values''']
              self.assertListEqual(arg_names[:1]     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__       ):
                   snake_case											=							self._prepare_for_class(__snake_case     ,   __snake_case       )
                   snake_case											=							model_class(__snake_case       )

                   @jax.jit
                   def model_jitted(__snake_case     ,   **__snake_case       ):
                        return model(pixel_values=__snake_case     ,   **__snake_case       )

                   with self.subTest('''JIT Enabled'''       ):
                        snake_case											=							model_jitted(**__snake_case       ).to_tuple()

                   with self.subTest('''JIT Disabled'''       ):
                        with jax.disable_jit():
                             snake_case											=							model_jitted(**__snake_case       ).to_tuple()

                   self.assertEqual(len(__snake_case       )     ,   len(__snake_case       )       )
                   for jitted_output, output in zip(__snake_case     ,   __snake_case       ):
                        self.assertEqual(jitted_output.shape     ,   output.shape       )




    @slow
    def 						a_     (			self       ):
         for model_class_name in self.all_model_classes:
              snake_case											=							model_class_name.from_pretrained('''google/vit-base-patch16-224'''       )
              snake_case											=							model(np.ones((1, 3, 2_2_4, 2_2_4)       )       )
              self.assertIsNotNone(__snake_case       )




